Bottom Line:
Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology.The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity.We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation.

Affiliation: Centre for Systems Biology, School of Biosciences, University of Birmingham, Birmingham, United Kingdom. k.j.alden@bham.ac.uk

ABSTRACTIntegrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis RToolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs.york.ac.uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website.

pcbi-1002916-g002: Use of spartan to determine the simulators robustness to parameter perturbation.A: A-Test scores for simulations perturbing the initial expression level of a chemoattractant. This parameter has a large effect on both simulation responses. B: A-Test scores for simulations perturbing the upper limit of chemoattractant expression, which when perturbed has no significant effect on simulation response. C: Distribution of cell displacement responses for the parameter perturbed in A.

Mentions:
Spartan produced the plots in Figure 2, where Figures 2(a,b) show the A-Test scores for an alteration in the values of two simulation parameters that model expression of chemoattractant molecules. The x-axis details the range of values explored and the y-axis shows the A test scores obtained by contrasting response values for perturbed parameter values with calibrated values. Figure 2(c) shows the effect that adjusting the value of the parameter in 2(a) has on cell displacement as a box-plot of response distributions. Results suggest that a change in the initial expression of chemoattractant molecules has a statistically significant effect on simulation response, and is more critical than the upper limit of expression, which has no statistically significant impact. This suggests that the initial expression level of chemoattractant molecules is an important factor influencing cell behaviour at this time-point in development. Laboratory investigations could then examine this experimentally through blocking chemokine expression at certain time-points in development, to determine if this prediction holds.

pcbi-1002916-g002: Use of spartan to determine the simulators robustness to parameter perturbation.A: A-Test scores for simulations perturbing the initial expression level of a chemoattractant. This parameter has a large effect on both simulation responses. B: A-Test scores for simulations perturbing the upper limit of chemoattractant expression, which when perturbed has no significant effect on simulation response. C: Distribution of cell displacement responses for the parameter perturbed in A.

Mentions:
Spartan produced the plots in Figure 2, where Figures 2(a,b) show the A-Test scores for an alteration in the values of two simulation parameters that model expression of chemoattractant molecules. The x-axis details the range of values explored and the y-axis shows the A test scores obtained by contrasting response values for perturbed parameter values with calibrated values. Figure 2(c) shows the effect that adjusting the value of the parameter in 2(a) has on cell displacement as a box-plot of response distributions. Results suggest that a change in the initial expression of chemoattractant molecules has a statistically significant effect on simulation response, and is more critical than the upper limit of expression, which has no statistically significant impact. This suggests that the initial expression level of chemoattractant molecules is an important factor influencing cell behaviour at this time-point in development. Laboratory investigations could then examine this experimentally through blocking chemokine expression at certain time-points in development, to determine if this prediction holds.

Bottom Line:
Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology.The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity.We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation.

Affiliation:
Centre for Systems Biology, School of Biosciences, University of Birmingham, Birmingham, United Kingdom. k.j.alden@bham.ac.uk

ABSTRACTIntegrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis RToolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs.york.ac.uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website.